[论文解读] Semantic-based Detection of Segment Outliers and Unusual Events for Wireless Sensor Networks

Lianli Gao, Michael Bruenig+1 more|arXiv (Cornell University)|Nov 9, 2014

Anomaly Detection Techniques and Applications参考文献 18被引用 7

一句话总结

本文提出SOUE-Detector，一种语义感知系统，通过结合动态时间规整（DTW）与本体驱动的语义推理，实现对无线传感器网络数据中段落异常值和异常事件的识别。该方法在春山国家公园环境传感器流数据中实现了高精度与高召回率，可借助Web门户实现高效搜索与可视化。

ABSTRACT

Environmental scientists have increasingly been deploying wireless sensor networks to capture valuable data that measures and records precise information about our environment. One of the major challenges associated with wireless sensor networks is the quality of the data and more specifically the detection of segment outliers and unusual events. Most previous research has focused on detecting outliers that are errors that are caused by unreliable sensors and sensor nodes. However, there is an urgent need for the development of new tools capable of identifying, tagging and visualizing erroneous segment outliers and unusual events from sensor data streams. In this paper, we present a SOUE Detector (Segment Outlier and Unusual Event-Detector) system for wireless sensor networks that combines statistical analyses using Dynamic Time Warping (DTW) with domain expert knowledge (captured via an ontology and semantic inferencing rules). The resulting Web portal enables scientist to efficiently search across a collection of wireless sensor data streams and identify, retrieve and display segment outliers (both erroneous and genuine) within the data streams. In this paper, we firstly describe the detection algorithms, the implementation details and the functionality of the SOUE Detector system. Secondly we evaluate our approach using data that comprises sensor observations collected from a sensor network deployed in the Springbrook National Park in Queensland, Australia. The experimental results show that the SOUE-Detector can efficiently detect segment outliers and unusual events with high levels of precision and recall.

研究动机与目标

• 解决无线传感器网络数据流中段落异常值与异常事件检测的挑战。
• 通过区分错误传感器读数与真实、有意义的异常，提升数据质量。
• 通过本体与语义推理整合领域专家知识，实现更精确的异常检测。
• 开发一个可搜索、可可视化Web门户，供环境科学家探索与分析检测到的异常。
• 使用春山国家公园的真实传感器数据评估系统性能。

提出的方法

• SOUE-Detector采用动态时间规整（DTW）比较传感器数据的时间段，基于与正常模式的偏离程度识别异常值。
• 利用领域特定本体应用语义推理，编码关于环境状况与预期行为的专家知识。
• 在本体上定义推理规则，基于上下文语义将检测到的段落分类为异常值或异常事件。
• 系统实时处理流式传感器数据，标记与参考模式显著偏离的段落。
• 实现Web门户，使科学家能够跨多个数据流搜索、检索并可视化检测到的异常值与异常事件。
• 该框架结合统计分析（DTW）与语义上下文，降低误报率并提升检测准确性。

实验结果

研究问题

• RQ1 如何在无线传感器网络数据流中有效检测段落异常值与异常事件？
• RQ2 语义推理在多大程度上可提升异常检测的准确性，超越纯统计方法？
• RQ3 结合DTW与基于本体的推理的混合方法能否提升异常检测的精确率与召回率？
• RQ4 SOUE-Detector在识别错误传感器读数与真实环境异常方面效果如何？
• RQ5 该系统能否支持环境科学家对检测到的异常进行高效搜索与可视化？

主要发现

• SOUE-Detector在真实传感器数据中检测段落异常值与异常事件方面实现了高精度与高召回率。
• 语义推理的整合显著提升了系统区分错误读数与有意义异常的能力。
• 该系统成功识别了春山国家公园传感器网络中的数据质量问题与罕见环境事件。
• Web门户使科学家能够高效地跨多个数据流搜索与可视化检测到的异常。
• 使用真实世界数据的实验评估表明，该系统在复杂环境监测场景中具有有效性与可扩展性。
• DTW结合语义推理的方法在降低误报率方面优于纯统计方法。